Field
The present disclosure relates generally to an electronic apparatus and a control method thereof, and for example, to an electronic apparatus performing image processing, and a control method thereof.
The present disclosure also relates to an artificial intelligence (AI) system simulating a recognition function and a decision function of a human brain using a machine learning algorithm, and an application thereof.
Description of Related Art
Recently, an artificial intelligence system implementing human-level intelligence has been used in various fields. The artificial intelligence system is a system in which a machine performs learning and decision and becomes smart by itself unlike an existing rule-based smart system. As the artificial intelligence system is used more, a recognition rate is improved and a user's taste may be more accurately understood, such that the existing rule-based smart system has been gradually replaced by a deep learning-based artificial intelligence system.
An artificial intelligence technology may include machine learning (for example, deep learning) and element technologies using the machine learning.
The machine learning may include an algorithm technology of classifying/learning features of input data by itself, and the element technology may include a technology of simulating functions such as recognition, decision, and the like, of a human brain using a machine learning algorithm such as deep learning, or the like, and may include technical fields such as linguistic understanding, visual understanding, inference/prediction, knowledge representation, a motion control, and the like.
Various fields to which the artificial intelligence technology may be applied are as follows. The linguistic understanding may refer to a technology of recognizing and applying/processing human languages, and may include natural language processing, machine translation, a dialog system, question and answer, speech recognition/synthesis, or the like. The visual understanding may refer to a technology of recognizing and processing things like human vision, and may include object recognition, object tracking, image search, human recognition, scene understanding, space understanding, image improvement, or the like. The inference/prediction may refer to a technology of deciding and logically inferring and predicting information, and may include knowledge/probability-based inference, optimization prediction, preference-based planning, recommendation, or the like. The knowledge representation may refer to a technology of automating and processing human experience information as knowledge data, and may include knowledge construction (data creation/classification), knowledge management (data utilization), or the like. The motion control may refer to a technology of controlling self-driving of a vehicle and a motion of a robot, and may include a motion control (navigation, collision, driving), a manipulation control (behavior control), or the like.
Meanwhile, a conventional image processing method may be divided into a non-leaning-based technology and a learning-based technology. The non-learning-based technology has an advantage that an image processing speed is rapid, but has a problem that flexible image processing depending on image characteristics is impossible. The learning-based technology has an advantage that flexible image processing is possible, but has a problem that real-time processing is difficult.
For example, considering a case of enlarging a resolution of an image, in an interpolation method, which is a representative method of the non-learning-based technology, a brightness of a pixel corresponding to a position at which the resolution is enlarged is calculated using a filter having low pass characteristics. In detail, there is a bi-cubic interpolation manner based on a spline, a resampling manner using a Lanczos filter formed by simplifying an ideal low-pass filter (Sinc Kernel), or the like. Such a non-learning-based technology shows stable image enlarging performance due to a low complexity, but may not reflect prior information possessed by only an image, such that edge sharpness is blurred, an edge is jagged, aliasing or ringing occurs in the vicinity of the edge.
As a representative method of the learning-based technology, there are a manner of directly using a high image quality image database for reconstruction, a manner of learning and using a high resolution conversion rule for each classified class, a manner of learning low resolution/high resolution conversion in an end-to-end mapping form by a deep learning network and enlarging the image using a learned network at the time of enlarging the image.
In the learning-based technologies, unique characteristics of an image signal are reflected in learning and are used at the time of enlarging the image, and the learning-based technologies may thus reconstruct a sharp, non-jagged, and smooth edge as compared with non-learning-based image enlarging methods. However, the learning-based technologies are appropriate for applications requiring non-real time due to a high complexity, but it is difficult to apply the learning-based technologies to apparatuses requiring real time, such as a television (TV). In addition, it is difficult to apply the learning-based technologies to system-on-chip (Soc) implementation for real time implementation.
In addition, the learning-based technologies show excellent performance with respect to edge components of which region features are clear, but show noise components with respect to a flat region of which a feature is unclear or show low performance on a detail representation surface. In addition, the learning-based technologies have a problem that the image may not be enlarged with respect to a non-learned magnification.
Therefore, it has been required to develop a technology capable of performing flexible image processing and improving an image processing speed.